This field-based case is an efficient vehicle for exposing students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood. Although the name of the company and individuals are disguised, the data are real and adjusted by an unspecified constant so that all relationships are preserved.